Distributed on-line network monitoring for trust assessment

Abstract : Collaborative systems are growing in use and in popularity. The need to boost the methods concerning the interoperability is growing as well; therefore, trustworthy interactions of the different systems are a priority. Trust as a computer science concept has been studied in the recent years. Nevertheless, in the literature, very little focus is placed on how to assess the correctness of the interactions between the entities; even if most approaches rely on the estimation of trust based on the accumulated measures of these values. To broadly determine the correctness of interactions without targeting a specific domain or application, an approach using Distributed On-line Network Monitoring (DONM) was proposed. Furthermore, a prototype tool-set was developed to automatically test the trust properties. DONM is a form passive testing; it analyzes systems' responses and test the correctness of the interactions via network traces. Since it relies on the stated properties to test, a novel approach was proposed to automatically extract relevant properties to test. Our approach deeply relies on the operation of On-line Monitoring Systems. That is the reason why we propose new methods to enhance the state of the art techniques to: a) efficiently evaluate properties in O(n) time complexity using an Extended Finite State Automata (EFSA) auxiliary data structure; and b) to expand the language expressiveness to properly express the constraints of such systems, such as, timeouts in order to avoid resource starvation. Finally, using the evaluation of the entities' interactions provided by our approach, trust management engines will help trustors to decide with whom and how to interact with other users or applications. We propose a new framework that is flexible for any domain, allowing trustors to define the trust features used to evaluate trustees in different contexts. Furthermore, with the evaluations of the trust features, we propose a trust model which achieves close-to-human inference of the trust assessment, by using a machine learning based trust model, namely solving a multi-class classification problem using Support Vector Machines (SVM). Using the SVM-based trust model, experiments were performed with simulated trust features to estimate trust level; an accuracy of more than 96% was achieved
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Jorge López. Distributed on-line network monitoring for trust assessment. Networking and Internet Architecture [cs.NI]. Université Paris-Saclay; Universidad Galileo, 2015. English. ⟨NNT : 2015SACLL003⟩. ⟨tel-01256611⟩

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